USING BAYESIAN STATISTICS FOR KERATOCONUS PROGRESSION PREDICTION
Published 2025 - 29th ESCRS Winter Meeting
Reference: PO032 | Type: Free Paper | DOI: 10.82333/xpsn-qg30
Authors: Andreas Honeder* 1 , Leon Pomberger 1 , Klemens Waser 1 , Haidar Khalil 1 , Matthias Bolz 1 , Peter Laubichler 1 , Nino Hirnschall 1
1Ophthalmology and Optometry,Kepler University Clinic, Johannes Kepler University ,Linz,Austria
Purpose
Aim of this study was to predict the progression of keratoconus using a novel artificial intelligence prediction model trained with data of 12 month follow ups.
Setting
Kepler University Clinic Linz, MCIII, Department of Ophthalmology and Optometry and Johannes Kepler University.
Methods
Patient data was collected from our Keratoconus outpatient clinic in a prospective fashion. In all cases a Scheimpflug measurement device (Pentacam HR, Oculus, Germany) and a spectral domain OCT (MS-39, CSO, Italy) were available at least over a time period of 12 months. Progression was defined as an increase of Kmax of ≥1 Diopter. Bayes statistic was used to find the best predictive parameters plus a machine learning approach to create an image based AI model.
Results
In total 345 eyes of 173 patients were included in this study. Mean age was 33.13 years (SD 13.56; median: 30; range 8 to 74). Preliminary results showed a Kmax of 52.47 D (SD: 7.85) and a mean tomographic astigmatism of 3.08 D (SD: 2.12). Thinnest pachymetry was on average 470.64μm (SD: 65.10). Of the different models the Bayesian progression model was found to be promising as well as a random forest machine learning approach.
Conclusion
Prediction of keratoconus progression using Bayesian statistics is a powerful tool that could be used additionally to “conventional” machine learning concepts.